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Coalition Formation in Dynamic Multicriteria Games

  • Mathematical Game Theory and Applications
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Abstract

In this paper, new approaches to obtain optimal behavior in dynamic multicriteria games are suggested. The multicriteria Nash equilibrium is designed via the Nash bargaining scheme (Nash products), and the cooperative equilibrium is determined by the Nash bargaining solution for the entire planning horizon. The process of coalition formation in dynamic mul-ticriteria games is studied. For constructing the characteristic function the Nash bargaining scheme is applied, where the multicriteria Nash equilibrium plays the role of the status quo points. Two modifications of the characteristic function are presented that take into account the information structure of the game (the models without and with information). The dynamic multicriteria bioresource management problem is considered. The players’ strategies and the quantities of resource are compared under the cooperative and noncooperative behavior for the above modifications of the characteristic function.

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Acknowledgements

This work was supported by the Shandong Province “Double-Hundred Talent Plan” (no. WST2017009) and the Russian Foundation for Basic Research (projects nos. 16-01-00183_a, 16-41-100062 r_a).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Qingdao University, Qingdao, 266071, PR China

    A. N. Rettieva

  2. Institute of Applied Mathematics of Shandong, Qingdao, 266071, PR China

    A. N. Rettieva

  3. Institute of Applied Mathematical Research, Karelian Research Center, Russian Academy of Sciences, Petrozavodsk, Russia

    A. N. Rettieva

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  1. A. N. Rettieva
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Correspondence to A. N. Rettieva.

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Russian Text © The Author(s), 2018, published in Matematicheskaya Teoriya Igr i ee Prilozheniya, 2018, No. 2, pp. 40–61.

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Rettieva, A.N. Coalition Formation in Dynamic Multicriteria Games. Autom Remote Control 80, 1912–1927 (2019). https://doi.org/10.1134/S0005117919100114

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  • DOI: https://doi.org/10.1134/S0005117919100114

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